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The article describes the possibility of applying artificial intelligence in broad scientific research in the field of magnetic abrasive finishing. Artificial intelligence makes it possible to accelerate research work at certain stages, which reduces costs. A prerequisite for its use is the formulation of appropriate queries and limiting the range of responses. These limitations are mainly due to the lack of access to scientific databases. The paper uses OpenAI software called Chat GPT and describes the specifics of the work. The paper examines the various aspects of creating a scientific study starting with selecting a topic, reviewing the literature, developing a research methodology, developing and analyzing the results, and drawing conclusions. The topic of intellectual property and the originality of the results and conclusions obtained was also touched upon. On this basis, a comparison was prepared showing the areas of scientific activity where the use of artificial intelligence is most effective. [ABSTRACT FROM AUTHOR]

Researchers are currently paying increased attention to the problem of green supplier selection because it affects the performance of green supply performance. However, the previous article never discussed the literature review on Green Supplier Selection for the Agri-food Industry. Therefore, the purpose of this paper is to provide a thorough analysis of the methodology and context of Green Supplier Selection for the Agri-food Industry. From 2014 to 2022, 28 peer-reviewed papers were collected from the Scopus database and Google Scholar searches. This review study is divided into two problem contexts: green supplier selection and green supplier selection and order allocation. Furthermore, this review is based on a methodology that incorporates problem-solving techniques such as Multi Criteria Decision Making (MCDM), Mathematical Programming (MP), Artificial Intelligence (AI), and Hybrid (Integrating MCDM-MP and MCDM-AI). The results show that the Green Supplier Selection problem has received the most attention, followed by the Green Supplier Selection & Order Allocation problem. Furthermore, the MCDM procedure dominates the methodology used in the Green Supplier Selection. [ABSTRACT FROM AUTHOR]

Industry 4.0 is transforming the manufacturing industry through the integration of advanced technologies such as automation and information technology. This paper provides a comprehensive review of the impact of Industry 4.0 on automation and information technology in industry. It examines the potential benefits of Industry 4.0, including improved efficiency, productivity, flexibility, and customization, as well as the challenges businesses face in implementing these technologies. The paper discusses the need for careful planning, collaboration, and investment to ensure that Industry 4.0 investments align with business goals and provide a return on investment. Also, this paper highlights the need to prioritize cyber security and training to mitigate the risks associated with integrating advanced technologies. It identifies the importance of reskilling and upskilling the workforce to prepare for new roles and responsibilities and highlights the need to prioritize cybersecurity in Industry 4.0 strategies. The paper highlights the opportunities that Industry 4.0 offers businesses to create new business models and transform their manufacturing operations. The use of advanced technologies such as 3D printing, artificial intelligence and the Internet of Things enables businesses to create products on demand and tailor them to specific customer needs. This can increase customer satisfaction and create new business opportunities. Alongside this, this paper presents an analysis of the impact of Industry 4.0 on various industries and the growth in adoption of Industry 4.0 technologies over time. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) with mathematical modelling is one of the most metaphoric technologies in modern history. It helps businesses around the world, improving efficiency and optimizing resources especially in supply chain management (SCM) and logistics sector. AI has also made its way into supply chains and logistics, where it provides several advantages to businesses that are prepared to accept new technology. The review refers to the previous research papers with keywords of AI, supply chain, logistics are sorted from five recent years and aims to comprehensively review types of technologies used by optimizing the strategies and the implementation of AI methods highlight several challenges faced by AI to be deployed in this sector. The soft computing extracted in this paper mainly uses AI and fuzzy logic for problem giving and enhances the efficiency of SCM. However, there are challenges that are encountered by AI to predict and drive the decision making with minimum complications. Several challenges addressed in this paper are resources, lack of security especially in RFID systems and the complexity found in robotics systems in logistics. In conclusion, based on the literature review, the research framework, new research based on the research gap is able to be obtained for further research focus. [ABSTRACT FROM AUTHOR]

Natural disasters cause devastation, chaos, destruction, death, displacement, and much more when they occur. With the advancement of recent technologies especially in artificial intelligence and deep learning, the severity and impact of natural disasters could be reduced by predicting their occurrence. In this research, the flood detection (using images) system is developed using the proposed architecture of the Deep Convolutional Neural Network (CNN). In this paper, the main aim is to propose an automated System for precision disaster detection, in particular flood detection. The intelligent system will monitor vast problematic areas that are dubbed flood risk areas during the season. The intelligent automated system will systematically take images with real-time processing to detect floods. If a flood is detected, the system will automatically communicate to the ground station with the early warning to issue an early warning to all the inhabitants in the path of the flood. Since this paper will prove a concept, the other main contribution of this paper is a new dataset consisting of 9000 images collected from various sources on the Internet, labeled, and preprocessed. The Deep CNN architectures were trained and tested by constructing a new dataset of more than 9000 labeled images with flood and non-flood collected from different sources. Several experiments are performed with changing CNN architectural design parameters in order to get the best recognition rates. Experimental results show that 92.50% accuracy was achieved using GoogleNet. The second highest performance was achieved using AlexNet with an accuracy of 92.20%. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is an essential component in modern games. With AI, players can feel the challenges in the game and the game will feel more real. AI has several branches, one of which is path-finding. Pathfinding is a way of finding the shortest path between two points. The main problem in path-finding is how to perform a path-finding accurately and requires fewer computational resources (CPU and memory). This paper describes the results of research that tested the A* and Hierarchical Pathfinding A* algorithms using the Unity 3D platform and the C# programming language. The graph or space to be used is a graph with 8 branch nodes. While the benchmarks used are the path generated and the processing time of an algorithm. This paper results in a conclusion that the path generated by the A* algorithm is shorter than the Hierarchical Pathfinding A* algorithm. The number of paths processed for the A* algorithm is more than the Hierarchical Pathfinding A* algorithm. The total execution time of the A* Algorithm is smaller than that of the A* Hierarchical Pathfinding Algorithm. The Hierarchical Pathfinding A* algorithm experiences spikes in execution time more often than the A* algorithm. However, if the total spike time, the Hierarchical Algorithm is less than the A* Algorithm. [ABSTRACT FROM AUTHOR]

This paper gives the idea of solving some of the problems faced by farmers. The population rate of the world is growing rapidly and will double in the coming decades and the need of food is also increasing simultaneously. To meet this rapid growth in demand for food, automation in agriculture is the best solution. The aim of this paper is to design and develop an automated system to monitor the various conditions of soil and moisture on the crops, and the proper supply of water, which affects the crops [5]. Such a system will contain sensors, a pump and a device called a humidifier, and various advanced techniques like IoT, machine learning, and artificial intelligence. The technical field is racing to provide more optimum solutions. The incorporation of IoT, along with cloud computing, big data analytics, and wireless sensor networks can provide enough scope to predicrocess, analyze situations and improve activities in a real-time situation. Green House is one of the best solutions as the crops can be grown in a controlled climate without the natural environmental conditions. In the last few decades there has been a huge advancement in technology for agriculture, with the help of technology we can help farmers for increasing their rate of yielding. With this, we can overcome the major problems of farmers. This small processor-based unit can be used to control anything from a fan, air-conditioner, humidifier, and watering system from 4 relay outputs. A temperature and humidity range can be set to control relay outputs. The main focus of this paper is to implement the Internet of Things (IoT) in the monitoring of humidity, soil condition, temperature, supply of water to the field, level of water, environmental condition, etc. The IoT-based Smart agriculture System being planned via this paper is integrated with different Sensors. [ABSTRACT FROM AUTHOR]

Speech emotion recognition is actually a challenging and new issue within artificial intelligence discipline. One of the primary bottlenecks of daily speech emotion identification strategies is actually tonal perturbation of speech. The goal of this paper is actually using Support Vector Machine (SVM) classifier to categorizing 7 various feelings like anger, happiness, sad, disgust, normal, boredom and fear. The exploration of futures capabilities consists of pitch, Mel-frequency spectrum coefficients (MFCC), Spectro temporal characteristics, formants as well as power specifications. Within this paper we utilize 3 distinct Tamil emotional collection as well as attribute fusion method to blend all of the spatial element extractions. Performance examination is actually accomplished by utilizing the confusion matrix as well as the precision rate attained by 3 databases are actually 87.5%, 86.25% & 88.5&% and having a typical average accuracy pace of 87.4%. And finally, from the outcomes for spatial feature fusion method on various databases are actually compared and we examined SVM recognition exhibits much better functionality with the very best precision rates in database 3 with 90% in 10-fold for the collected Tamil Emotional database. [ABSTRACT FROM AUTHOR]

Machine learning is an artificial intelligence (AI) technique for automation and prediction that is widely utilized today. This paper looks at the fledgling usage of machine learning in the legal field. The first portion focuses on the fundamentals of machine learning. The relationship between machine learning and law is discussed in the next section. Many parts of the law can be interpreted in terms of data, and machine learning works by recognizing patterns in data. Machine learning technologies can be used to learn about data-driven aspects of the law. After that, this paper looks at how machine learning is used in law while also recognizing the technology's strengths and limitations. It finishes by examining some current social debates around the use of machine learning in legal contexts. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study of the trends in banking services. The consequences of the severe post-Covid-19 crisis and the isolation have had a strong impact on all areas of business. This has led to a rapid digitization and implementation of smart bank branch technology, aiming at saving financial resources. Transactional models, representing the participants in a mobile payment transaction, as well as the connections, by means of which the communication takes place, are considered. The application of artificial intelligence in the bank activities and in the activities of the new bank branches is analyzed. The trends for future development of both the bank branches and the banking operations are outlined. [ABSTRACT FROM AUTHOR]